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An artificial neural network identification method for thermal resistance of exterior walls of buildings based on numerical experiments

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Abstract

Current standards for the detection methods for the thermal resistance of exterior walls of buildings have shortcomings, such as strict conditions, high time consumption, and heavy workloads. To overcome the shortcomings of existing methods, in this study, an artificial neural network identification method was used to detect the thermal resistance of exterior walls. To enhance efficiency and reduce costs, the data required by the neural network modelling were obtained through a numerical experiment based on an unsteady heat transfer model. In this paper, the thermal resistance identification results of three neural networks-Back Propagation (BP), Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN)-were analysed and compared. The results demonstrated that the GRNN neural network had the best identification effect. Thus, the identification system for the thermal resistance of exterior walls was established using the GRNN neural network. The average test error in the training sample was 0.098%, and the average error in the anti-noise test was 4.82%. The network identification accuracy was verified by five groups of field measured data. In comparison with the conventional heat flux method, the average error was 5.82%, which proved the reliability of the proposed GRNN identification model.

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Acknowledgements

The authors would like to acknowledge the financial support provided for this research by the National Natural Science Foundation of China (No. 51778168 and No. 51478136), the Project of Applied Technology Research and Development Program of Heilongjiang Province (No. GZ15A505), and the Autonomous Research Foundation Project of Cold Region Building Science Key Laboratory of Heilongjiang Province (No. 2016HDJZ-1106).

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Authors and Affiliations

  1. School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xidazhi Street, Harbin, 150001, China

    Lin Chen, Changhong Zhan & Guanghao Li

  2. College of Civil Engineering, Northeast Forestry University, Harbin, 150001, China

    Aimin Zhang

Authors
  1. Lin Chen
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  2. Changhong Zhan
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  3. Guanghao Li
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  4. Aimin Zhang
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Correspondence to Changhong Zhan or Guanghao Li.

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Chen, L., Zhan, C., Li, G. et al. An artificial neural network identification method for thermal resistance of exterior walls of buildings based on numerical experiments. Build. Simul. 12, 425–440 (2019). https://doi.org/10.1007/s12273-019-0524-6

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  • DOI: https://doi.org/10.1007/s12273-019-0524-6

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